Natural Language Processing with Python#

This section includes notes on natural language processing with Python. More specifically, it is about extracting meaningful structures and patterns from massive collections of texts.

Topics may include:

  • Text Normalization Techniques

  • Important Python libraries for NLP

  • Traditional (Shallow) Machine Learning

  • Text Classification

  • Text Summarization

  • Topic Modeling

  • Text Similarity and Clustering

  • Semantic Analysis and Network

  • Sentiment Analysis

  • Word Embeddings and Deep Learning

  • Recurrent Neural Network and LSTM

  • Sequence-to-Sequence and Machine Translation

  • Transfer Learning Using BERT

  • Hyper-Parameter Tuning (Grid Search and Cross Validation)

  • Explainable AI (LIME)